Statistics and Business Department, University of Cordoba, Cordoba, Spain.
Organic Chemistry Department, University of Cordoba, Cordoba, Spain.
J Sci Food Agric. 2019 May;99(7):3417-3425. doi: 10.1002/jsfa.9559. Epub 2019 Feb 10.
Fatty acids are the major components in extra virgin olive oil, and they are considered as a quality parameter to its authentication. As fraudulent practices are the most important problem in this sector, fast, reliable and cost-effective techniques, such as Raman spectroscopy, have been successfully applied, in combination with chemometrics, to determine the fatty acid profile of oils.
The huge amount of information provided by Raman spectra is reduced in a few orthogonal components of principal component analysis (PCA). The goodness-of-fit of the statistical models including only these PCA factors is considerably increased by introducing dummy variables, associated with the harvest, and some agro-climatic variables (temperature, humidity, wind speed, radiation, precipitation and evapotranspiration). Many of these additional variables are statistically relevant in models using either the global sample or subsamples of Andalusian provinces or olive varieties.
The regression models using only Raman spectral information are clearly improved by the consideration of harvesting time and agro-climatic data, a useful result as trade standard applying to olive oils limits values for disaggregated fatty acids to authenticate olive oils. Nevertheless, the effect (or the statistical relevance) of these variables depends on the specific type of fatty acid, geographical region (province) or olive variety. © 2019 Society of Chemical Industry.
脂肪酸是特级初榨橄榄油的主要成分，被认为是其鉴别的质量参数。由于欺诈行为是该行业最重要的问题，因此已经成功地应用了拉曼光谱等快速、可靠和具有成本效益的技术，并结合化学计量学来确定油的脂肪酸分布。
拉曼光谱提供的大量信息在主成分分析（PCA）的少数几个正交分量中被减少。通过引入与收获和一些农业气候变量（温度、湿度、风速、辐射、降水和蒸散）相关的虚拟变量，统计模型的拟合度显著提高，包括这些 PCA 因子。在使用整个样本或安达卢西亚省或橄榄品种的子样本的模型中，许多这些附加变量在统计学上是相关的。
仅使用拉曼光谱信息的回归模型通过考虑收获时间和农业气候数据得到了明显的改进，这是一个有用的结果，因为适用于橄榄油的贸易标准将离散脂肪酸的限值应用于橄榄油的鉴别。然而，这些变量的影响（或统计学相关性）取决于特定类型的脂肪酸、地理区域（省份）或橄榄品种。© 2019 化学工业协会。
